In [122]:
import pandas as pd
import numpy as np 
import matplotlib.pyplot as plt
# import seaborn as sns 
import datetime
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from plotly.graph_objs import Scatter, Figure, Layout
import plotly
import plotly.graph_objs as go
import plotly.express as px
from IPython.display import Markdown as md
init_notebook_mode(connected=False)
import io
import requests
import re

COVID-19 in Italy. Visuals


(alternatively, see results and code together here)

 


Data source: this GitHubi page

Authors and sources mentioned: Editore/Autore del dataset: Dipartimento della Protezione Civile. Categoria ISO 19115: Salute. Dati forniti dal Ministero della Salute.

Regional data files (Dati per Regione):
  • Struttura file giornaliero: dpc-covid19-ita-regioni-yyyymmdd.csv (dpc-covid19-ita-regioni-20200224.csv)
  • File complessivo: dpc-covid19-ita-regioni.csv
  • File ultimi dati (latest): dpc-covid19-ita-regioni-latest.csv

 

In [123]:
URL='https://it.wikipedia.org/wiki/Regione_(Italia)'
res=requests.get(URL)
tables=pd.read_html(res.text)
dt = tables[13]
In [124]:
def dewhite(x):
    ''.join(re.findall('\d+', x))

dt2 = dt[['Regione','Popolazione (ab.)']].copy()
dt2.columns = ['Region','Pop']
    
dt2.Pop = dt2.Pop.apply(lambda x: ''.join(re.findall('\d+', x))).astype(int)
In [125]:
s = requests.get("https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni.csv").content
dat = pd.read_csv(io.StringIO(s.decode('utf-8')))
cdate = dat.data.max()

md("Currently data as of date: {}".format(cdate))
Out[125]:

Currently data as of date: 2020-10-21T17:00:00


 

What's in the original dataframe?

In [126]:
md("All column names: {}".format(dat.columns.tolist()))
Out[126]:

All column names: ['data', 'stato', 'codice_regione', 'denominazione_regione', 'lat', 'long', 'ricoverati_con_sintomi', 'terapia_intensiva', 'totale_ospedalizzati', 'isolamento_domiciliare', 'totale_positivi', 'variazione_totale_positivi', 'nuovi_positivi', 'dimessi_guariti', 'deceduti', 'casi_da_sospetto_diagnostico', 'casi_da_screening', 'totale_casi', 'tamponi', 'casi_testati', 'note']

In [127]:
df = dat.drop(['stato','codice_regione'], axis=1)
df.columns = ['Date','Region','Lat','Long','HospWithSymptoms','IC','HospTotal','AtHome','CurrentlyPositive','VariationOfPositives','NewPositives','Recovered', 'Deaths','Diagnostico','Screening','TotalCases','NoOfTests','casi_testati','note']

df = pd.merge(df, dt2, left_on='Region', right_on='Region')

df['Date'] = pd.to_datetime(df['Date']).dt.date
df = df.set_index(df["Date"])
df.index = pd.to_datetime(df.index)

df['NewPositives'] = np.abs(df['NewPositives'])

dat.tail(5)
Out[127]:
data stato codice_regione denominazione_regione lat long ricoverati_con_sintomi terapia_intensiva totale_ospedalizzati isolamento_domiciliare ... variazione_totale_positivi nuovi_positivi dimessi_guariti deceduti casi_da_sospetto_diagnostico casi_da_screening totale_casi tamponi casi_testati note
5056 2020-10-21T17:00:00 ITA 19 Sicilia 38.115697 13.362357 565 83 648 7202 ... 353 562 5551 389 8879.0 4911.0 13790 614264 437540.0 NaN
5057 2020-10-21T17:00:00 ITA 9 Toscana 43.769231 11.255889 503 76 579 11660 ... 693 866 12006 1221 20011.0 5455.0 25466 950782 639007.0 NaN
5058 2020-10-21T17:00:00 ITA 10 Umbria 43.106758 12.388247 152 20 172 2783 ... 280 350 2403 95 2183.0 3270.0 5453 262392 154074.0 NaN
5059 2020-10-21T17:00:00 ITA 2 Valle d'Aosta 45.737503 7.320149 42 5 47 784 ... 106 111 1155 146 1913.0 219.0 2132 35990 23483.0 NaN
5060 2020-10-21T17:00:00 ITA 5 Veneto 45.434905 12.338452 439 56 495 10938 ... 1177 1422 24550 2282 23338.0 14927.0 38265 2178114 849385.0 NaN

5 rows × 21 columns


 

Variable names to English and their explanation

  • HospWithSymptoms : Currently hospitalized patients with symptoms
  • IC : Intensive care
  • HospTotal: Total number of currently hospitalized patients
  • AtHome : Currently at home confinement
  • CurrentlyPositive : Total amount of current positive cases (Hospitalised patients + Home confinement)
  • NewPositives : New amount of positive cases (Actual total amount of current positive cases - total amount of current positive cases of the previous day)
  • TotalCases : Total amount of positive cases
  • NoOfTests : Tests performed
In [128]:
df.tail()
Out[128]:
Date Region Lat Long HospWithSymptoms IC HospTotal AtHome CurrentlyPositive VariationOfPositives NewPositives Recovered Deaths Diagnostico Screening TotalCases NoOfTests casi_testati note Pop
Date
2020-10-17 2020-10-17 Veneto 45.434905 12.338452 328 43 371 8369 8740 602 774 24064 2247 22758.0 12293.0 35051 2135650 832701.0 NaN 4905854
2020-10-18 2020-10-18 Veneto 45.434905 12.338452 378 44 422 9003 9425 685 800 24170 2256 22849.0 13002.0 35851 2145935 836440.0 NaN 4905854
2020-10-19 2020-10-19 Veneto 45.434905 12.338452 396 44 440 9405 9845 420 502 24253 2255 22902.0 13451.0 36353 2150361 838391.0 NaN 4905854
2020-10-20 2020-10-20 Veneto 45.434905 12.338452 459 51 510 9746 10256 411 490 24319 2268 22987.0 13856.0 36843 2158487 841548.0 NaN 4905854
2020-10-21 2020-10-21 Veneto 45.434905 12.338452 439 56 495 10938 11433 1177 1422 24550 2282 23338.0 14927.0 38265 2178114 849385.0 NaN 4905854

 

daily numbers & moving averages (MA)

(double click and click on legend to select one or multiple regions in the graph)

In [129]:
df2 = df

fig = px.line(df2, x=df2.index, y="NewPositives", color="Region", hover_name="Region",
        render_mode="svg", log_y=False)
fig.update_layout(title="Daily new positive cases")
fig.show()
In [130]:
df2['MovAv7'] = df2['NewPositives'].rolling(window=7).mean()

fig = px.line(df2[df2.index>'2020-3-1'], x=df2.index[df2.index>'2020-3-1'], y="MovAv7", color="Region", hover_name="Region",
        render_mode="svg", log_y=False)
fig.update_layout(title="7-day MA of new positive cases")
fig.show()
In [131]:
df2['NewPos_pc'] = df2['NewPositives']/df2['Pop']*1000_00

df2['NewPos_pc'] = df2['NewPos_pc'].rolling(window=7).mean()

fig = px.line(df2[df2.index>'2020-3-1'], x=df2.index[df2.index>'2020-3-1'], y="NewPos_pc", color="Region", 
              hover_name="Region", log_y=False)
fig.update_layout(title="7-day MA of new positive cases, per 100K")
fig.show()
In [132]:
df2['IC_pc'] = df2['IC']/df2['Pop']*1000_00

fig = px.line(df2, x="Date", y="IC_pc", color="Region", hover_name="Region",
        render_mode="svg", log_y=False)
fig.update_layout(title="Current number of intensive care patients, per 100K")
fig.show()
In [133]:
df2['Hosp_pc'] = df2['HospTotal']/df2['Pop']*1000_00

fig = px.line(df2, x="Date", y="Hosp_pc", color="Region", hover_name="Region",
        render_mode="svg", log_y=False)
fig.update_layout(title="Current number of hospitalized, per 100K")
fig.show()
In [134]:
df3 = df2.copy()

df3['NewDeaths'] = df3['Deaths'] - df3.groupby(['Region'])['Deaths'].transform('shift')

fig = px.bar(df3, x=df3['Date'], y="NewDeaths", color="Region", hover_name="Date")
fig.update_layout(title="Daily number of deaths")
fig.show()
In [135]:
df2['Deaths_per_mio'] = (df2['Deaths']/df2['Pop'])*1000_00
fig = px.line(df2, x="Date", y="Deaths_per_mio", color="Region", 
              hover_name="Region", render_mode="svg", line_shape='spline')
fig.update_layout(title="Cumulative number of deaths, per 100K")
fig.show()
In [136]:
df2['Change_per_mio'] = df2['VariationOfPositives']/df2['Pop']*1000_00
df2['Change_per_mio'] = df2['Change_per_mio'].rolling(window=7).mean()

fig = px.line(df2[(df2.index>'2020-3-1') & (df2['Region']!="""Valle d'Aosta""")], x='Date', y="Change_per_mio", color="Region", hover_name="Date")
fig.update_layout(title="7-day MA of change in current positive cases, per 100K (excl. Valle d'Aosta)")
fig.show()
In [137]:
df2['Current_per_mio'] = df2['CurrentlyPositive']/df2['Pop']*1000_00
df2['Current_per_mio'] = df2['Current_per_mio'].rolling(window=14).mean()

fig = px.line(df2[(df2.index>'2020-3-7')], x='Date', y="Current_per_mio", color="Region", hover_name="Date")
fig.update_layout(title="14-day MA of current positive cases, per 100K")
fig.show()

 

All regions together

In [138]:
df2 = df
df_sum = df2.drop(['Lat','Long'], axis=1).groupby(df.Date).sum().reset_index()

df_sum2 = pd.melt(df_sum, id_vars=['Date'], value_vars=['NewPositives','IC','HospTotal'])

fig = px.line(df_sum2, x="Date", y="value", color='variable', hover_name="value", render_mode="svg", log_y=True, 
              line_shape='spline')
fig.update_layout(title="Number of new positive cases, current IC patients and currently hospitalized")
fig.show()
In [ ]: